Instructions to use LesterCerioli/LLM-GO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LesterCerioli/LLM-GO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LesterCerioli/LLM-GO")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LesterCerioli/LLM-GO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LesterCerioli/LLM-GO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LesterCerioli/LLM-GO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LesterCerioli/LLM-GO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LesterCerioli/LLM-GO
- SGLang
How to use LesterCerioli/LLM-GO with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "LesterCerioli/LLM-GO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LesterCerioli/LLM-GO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "LesterCerioli/LLM-GO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LesterCerioli/LLM-GO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LesterCerioli/LLM-GO with Docker Model Runner:
docker model run hf.co/LesterCerioli/LLM-GO
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a58ece3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """CLI: interactive Go code generation."""
import click
import tensorflow as tf
from llm_go.model.transformer import GoLLM
from llm_go.tokenizer.go_tokenizer import GoTokenizer
@click.command()
@click.option("--model-dir", required=True, help="Checkpoint directory")
@click.option("--tok-dir", required=True, help="Tokenizer directory")
@click.option("--prompt", default="", help="Prompt text (leave empty for interactive)")
@click.option("--max-tokens", default=256, show_default=True)
@click.option("--temperature", default=0.8, show_default=True)
@click.option("--top-p", default=0.95, show_default=True)
@click.option("--top-k", default=50, show_default=True)
def main(model_dir, tok_dir, prompt, max_tokens, temperature, top_p, top_k):
"""Generate Go code with a trained GoLLM checkpoint."""
tok = GoTokenizer.load(tok_dir)
model = GoLLM.from_pretrained(model_dir)
def generate(text: str) -> str:
ids = tok.encode(text)
out = model.generate(
tf.constant([ids], dtype=tf.int32),
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
)
return tok.decode(out[0].numpy().tolist())
if prompt:
click.echo(generate(prompt))
else:
click.echo("GoLLM interactive shell. Type 'exit' to quit.\n")
while True:
try:
text = click.prompt(">>> ")
except (EOFError, KeyboardInterrupt):
break
if text.strip().lower() in {"exit", "quit"}:
break
click.echo(generate(text))
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